Colour recipe optimisation for paint industry





    Ready for a bit of home improvement? Let’s paint your room. Warm shades of grey, for example. This may be a nice background to the art on the walls and the bookcases or shelves - or any furniture you have. Now that we have a general idea of what the color is going to be like, let’s go get some paint. This is not quite simple, though. Once we get to the store, we have to browse through dozens of swatches. All the imaginable shades of the color - and we realize, that there is a lot more to the color than we originally imagined. Would classic grey be OK or should we go for a blue-toned grey? 

    And now imagine that you are a department leader at a mechanical engineering company and have to paint your spare parts in the same procedure? Chances are, we are going to find a shade that is the closest to what we initially envisaged, but as we paint with it, still the color reflects the light in a way we couldn’t quite foresee or the ground makes it look too dark. And we go back to the supplier and repeat the ordering and mixing process up to 15 times, which at an industrial scale, is the average number of attempts.

    Alternatively, we can use ai-predict. The service calculates a variety of parameters, including the pigments, binders, solvents, and additives, the process of creating the paint, and the specific physical environment where the paint is going to be applied. Then the service then makes a suggestion about the paint composition and recipe.

    More on the website of